Using monte carlo simulation to mitigate the risk of project cost overruns
Bibliographic record
Abstract
Cost overruns are common on government and commercial projects. This paper proposes a cost risk estimating method that provides more accurate estimates of total project cost and answers the following important questions: (1) What is the most likely cost? (2) How likely is the baseline cost estimate to be overrun? (3) How much contingency is required on the project to guarantee that the total project cost is not to be exceeded, with a certain confidence level? The proposed method is based on the Monte Carlo simulation. It helps gain better information than traditional cost estimating methods, mainly because it recognizes that project costs are uncertain. A fictitious case study was developed to provide a structured way to provide the contingency value of a project in order to avoid cost overruns. Data were collected on low, most likely and high possible costs and the @Risk software from the Palisade Corporation was used to run the Monte Carlo simulations. Using a simplified cost case study, this paper demonstrates how Monte Carlo simulation can assist project managers in estimating the contingency to be allocated to their project, and contribute to fostering and bolstering the credibility of risk analysis results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".